Paste your text in here…Summary
The authors are pointing out the increase of huge quantity of information produced about and by people, things, and their interactions in a critical way. The authors based their six statements on the arguments presented by different groups, for example sociologists, scientists and economists. It is their intention to.spark.conversations.about issues of big data. Those groups discussed about the interaction with social media, government records, health records and other examples where people left digital traces. This article is focussed on social media, but those six statements are also important in several other fields. The keyword Big Data is defined in this article as a cultural, scholarly and technical phenomenon. Those terms are linked with the interaction of technology, mythology and analysis. They use Lissig(1999) argument, who mentions that social systems are regulated by four forces: law, market, social norms and architecture. But those forces are frequently in conflict with each other, each system tries to protect their point of view.
The first statement is about the changing of the definition of knowledge by Big Data. There is given an example where Fordism has been used. Ford made cars with the invention of automated and standardized production. Fordism was the mark of the industrial revolution, the human relationship between work and society has been changed. Big Data not only allude to large data sets, but also to a computational turn in though and research (Burkholder 1992). ‘Just as Ford changed the way we made cars, Big Data has emerged a system of knowledge that is already changing the objects of knowledge, while also having the power to inform how we understand human network and community’ (Boyd & Crawford,2012, p. 665). The society will be changed when you change the instruments. The authors mention that Big Data create an extreme shift in how people think about research. Ford changed the way how people though about work by inventing economic and social systems based on mass production. In this way knowledge can be developed by several factors, because Big Data has emerged a system of knowledge that is also changing the objects of knowledge.
The second statement mentions that claims to objectivity and accuracy are misleading. In this article there are some discussions about the scientific method and philosophy as research. The scientific method is objective. They based their results on analytical processes designed by the researcher and the view of the researcher. Unfortunately, the notion of objectivity has been a central issue for the philosophy of science. The claims to objectivity are misleading, because the results are based on observations and choices. Gitelman(2011) mentions that every analyser have his own way to analyse data. The result is that an experiment might seem valid, but there cannot be said that the interpretations are created equal. Analysers can misinterpret data, but data can also be inconsistent. It is important, for the analysers, where data comes from and if they are reliable. The conclusion is that data analysis is most effective when analysers are aware of the complex processes that underlie data analysis.
The next statement is about the size of big data. Bigger data is not always better. They use Twitter as an example. This example is already given in the introduction. Twitter is a great mass of data, but Twitter does not represent all people. The population who use Twitter is not representative for the global population. This is difficult for analysers who are not aware of the data quality that they are analysing, because actual information is not accessed.
The fourth statement is about Big Data and losing its meaning. Many models are made by reducing data, but the danger is that data loses its value. If the data is used out of context it will lose their meaning, but it is necessary to make mathematical models. For example, the authors introduce ‘articulated networks’ and behavioural networks’. Articulated networks.are.those.that results from people.specifying.their contacts through technical.mechanisms like email or cell phone address books. Behavioural.networks.are.derived from.communication patterns like sending a text message and sending an email to one another. But the data is not equal to personal networks. When the data suggest that someone spend more time with their colleague than their partner, that does not mean that the colleague is more important than the partner. Thus, data is not generic.
The fifth statement is about ethics. When analysing data is accessible, it does not mean it is ethical. For example, Facebooks’ anonymous data were released to the world. Analysers discovered to de-anonymize.parts of the data set. When data is publicly accessible, it does not mean that everyone can consume this data. There are better privacy protections needed, but privacy.breaches are hard to make specific. Many social media users are not aware that their data is being used, data may be public without the permission of the Facebook user or the user is not aware of the profits that come from their information. Analysers do not know the difference between ‘being in public’ and data what is ‘being public’. ‘Being in public’ does not have the intention to court attention.
The last statement is about creating a new digital divide by limited access to Big Data. It is about the division between those who have and who have not access to data in the system. The analysers, the smallest group, are the most privileged.
It seems reasonable to conclude that using Big Data can have a lot of consequences. People must not forget that using Big Data have lots of cons. They have to be aware of those cons when they are using social media such as Twitter and Facebook. Big Data can be seen as a powerful tool for resolving social problems, but the authors are pointing out that it can be an invasion of privacy and decreasing citizens’ freedom.